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Review fix 2.0

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OmriAx 2025-03-05 12:35:10 +02:00
parent aaad1938e4
commit 9bdc2d4457
3 changed files with 44 additions and 33 deletions
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25
docs/docs/configuration/object_detectors.md
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@ -156,12 +156,13 @@ detectors:
device: PCIe device: PCIe
model: model:
width: 640 width: 320
height: 640 height: 320
input_tensor: nhwc input_tensor: nhwc
input_pixel_format: rgb input_pixel_format: rgb
input_dtype: int input_dtype: int
model_type: hailo-yolo model_type: yolo-generic
# The detector automatically selects the default model based on your hardware: # The detector automatically selects the default model based on your hardware:
# - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef) # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
# - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef) # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
@ -172,7 +173,7 @@ model:
# path: /config/model_cache/hailo/yolov6n.hef # path: /config/model_cache/hailo/yolov6n.hef
# #
# You can also override using a custom URL: # You can also override using a custom URL:
# url: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/ssd_mobilenet_v2.hef # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
# just make sure to give it the write configuration based on the model # just make sure to give it the write configuration based on the model
``` ```
@ -197,7 +198,7 @@ model:
# path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
# #
# Or override using a custom URL: # Or override using a custom URL:
# url: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
``` ```
#### Custom Models #### Custom Models
@ -209,11 +210,6 @@ detectors:
hailo8l: hailo8l:
type: hailo8l type: hailo8l
device: PCIe device: PCIe
# Optional: Specify a local model path.
# path: /config/model_cache/hailo/custom_model.hef
#
# Alternatively, or as a fallback, provide a custom URL:
# url: https://custom-model-url.com/path/to/model.hef
model: model:
width: 640 width: 640
@ -221,14 +217,19 @@ model:
input_tensor: nhwc input_tensor: nhwc
input_pixel_format: rgb input_pixel_format: rgb
input_dtype: int input_dtype: int
model_type: hailo-yolo model_type: yolo-generic
# Optional: Specify a local model path.
# path: /config/model_cache/hailo/custom_model.hef
#
# Alternatively, or as a fallback, provide a custom URL:
# path: https://custom-model-url.com/path/to/model.hef
``` ```
For additional ready-to-use models, please visit: https://github.com/hailo-ai/hailo_model_zoo For additional ready-to-use models, please visit: https://github.com/hailo-ai/hailo_model_zoo
Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-processing. You're welcome to choose any of these pre-configured models for your implementation. Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-processing. You're welcome to choose any of these pre-configured models for your implementation.
> **Note:** > **Note:**
> If both a model **path** and **URL** are provided, the detector will first check the local model path. If the file is not found, it will download the model from the URL. > The config.path parameter can accept either a local file path or a URL ending with .hef. When provided, the detector will first check if the path is a local file path. If the file exists locally, it will use it directly. If the file is not found locally or if a URL was provided, it will attempt to download the model from the specified URL.
--- ---

3
frigate/detectors/detector_config.py
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@ -38,8 +38,7 @@ class ModelTypeEnum(str, Enum):
yolov9 = "yolov9" yolov9 = "yolov9"
yolonas = "yolonas" yolonas = "yolonas"
dfine = "dfine" dfine = "dfine"
hailoyolo = "hailo-yolo" yologeneric = "yolo-generic"
class ModelConfig(BaseModel): class ModelConfig(BaseModel):
path: Optional[str] = Field(None, title="Custom Object detection model path.") path: Optional[str] = Field(None, title="Custom Object detection model path.")

47
frigate/detectors/plugins/hailo8l.py
View File

@ -164,7 +164,7 @@ class HailoAsyncInference:
def callback(self, completion_info, bindings_list: List, input_batch: List): def callback(self, completion_info, bindings_list: List, input_batch: List):
if completion_info.exception: if completion_info.exception:
logging.error(f"Inference error: {completion_info.exception}") logger.error(f"Inference error: {completion_info.exception}")
else: else:
for i, bindings in enumerate(bindings_list): for i, bindings in enumerate(bindings_list):
if len(bindings._output_names) == 1: if len(bindings._output_names) == 1:
@ -236,15 +236,14 @@ class HailoDetector(DetectionApi):
self.cache_dir = MODEL_CACHE_DIR self.cache_dir = MODEL_CACHE_DIR
self.device_type = detector_config.device self.device_type = detector_config.device
# Model attributes should be provided in detector_config.model # Model attributes should be provided in detector_config.model
self.model_path = detector_config.model.path if hasattr(detector_config.model, "path") else None
self.model_height = detector_config.model.height if hasattr(detector_config.model, "height") else None self.model_height = detector_config.model.height if hasattr(detector_config.model, "height") else None
self.model_width = detector_config.model.width if hasattr(detector_config.model, "width") else None self.model_width = detector_config.model.width if hasattr(detector_config.model, "width") else None
self.model_type = detector_config.model.model_type if hasattr(detector_config.model, "model_type") else None self.model_type = detector_config.model.model_type if hasattr(detector_config.model, "model_type") else None
self.tensor_format = detector_config.model.input_tensor if hasattr(detector_config.model, "input_tensor") else None self.tensor_format = detector_config.model.input_tensor if hasattr(detector_config.model, "input_tensor") else None
self.pixel_format = detector_config.model.input_pixel_format if hasattr(detector_config.model, "input_pixel_format") else None self.pixel_format = detector_config.model.input_pixel_format if hasattr(detector_config.model, "input_pixel_format") else None
self.input_dtype = detector_config.model.input_dtype if hasattr(detector_config.model, "input_dtype") else None self.input_dtype = detector_config.model.input_dtype if hasattr(detector_config.model, "input_dtype") else None
self.url = detector_config.url
self.output_type = "FLOAT32" self.output_type = "FLOAT32"
self.set_path_and_url(detector_config.model.path)
self.working_model_path = self.check_and_prepare() self.working_model_path = self.check_and_prepare()
# Set up asynchronous inference # Set up asynchronous inference
@ -252,7 +251,7 @@ class HailoDetector(DetectionApi):
self.input_queue = queue.Queue() self.input_queue = queue.Queue()
self.output_queue = queue.Queue() self.output_queue = queue.Queue()
try: try:
logging.debug(f"[INIT] Loading HEF model from {self.working_model_path}") logger.debug(f"[INIT] Loading HEF model from {self.working_model_path}")
self.inference_engine = HailoAsyncInference( self.inference_engine = HailoAsyncInference(
self.working_model_path, self.working_model_path,
self.input_queue, self.input_queue,
@ -260,11 +259,23 @@ class HailoDetector(DetectionApi):
self.batch_size self.batch_size
) )
self.input_shape = self.inference_engine.get_input_shape() self.input_shape = self.inference_engine.get_input_shape()
logging.debug(f"[INIT] Model input shape: {self.input_shape}") logger.debug(f"[INIT] Model input shape: {self.input_shape}")
except Exception as e: except Exception as e:
logging.error(f"[INIT] Failed to initialize HailoAsyncInference: {e}") logger.error(f"[INIT] Failed to initialize HailoAsyncInference: {e}")
raise raise
def set_path_and_url(self, path: str = None):
if self.is_url(path):
self.url = path
self.model_path = None
else:
self.model_path = path
self.url = None
def is_url(self, url: str) -> bool:
return url.startswith("http://") or url.startswith("https://") or url.startswith("www.")
@staticmethod @staticmethod
def extract_model_name(path: str = None, url: str = None) -> str: def extract_model_name(path: str = None, url: str = None) -> str:
model_name = None model_name = None
@ -325,7 +336,7 @@ class HailoDetector(DetectionApi):
return model_path return model_path
def detect_raw(self, tensor_input): def detect_raw(self, tensor_input):
logging.debug("[DETECT_RAW] Starting detection") logger.debug("[DETECT_RAW] Starting detection")
# Pre process the input tensor # Pre process the input tensor
logger.debug(f"[DETECT_RAW] Starting pre processing") logger.debug(f"[DETECT_RAW] Starting pre processing")
@ -334,7 +345,7 @@ class HailoDetector(DetectionApi):
# Ensure tensor_input has a batch dimension # Ensure tensor_input has a batch dimension
if isinstance(tensor_input, np.ndarray) and len(tensor_input.shape) == 3: if isinstance(tensor_input, np.ndarray) and len(tensor_input.shape) == 3:
tensor_input = np.expand_dims(tensor_input, axis=0) tensor_input = np.expand_dims(tensor_input, axis=0)
logging.debug(f"[DETECT_RAW] Expanded input shape to {tensor_input.shape}") logger.debug(f"[DETECT_RAW] Expanded input shape to {tensor_input.shape}")
# Enqueue input and a sentinel value # Enqueue input and a sentinel value
self.input_queue.put(tensor_input) self.input_queue.put(tensor_input)
@ -344,11 +355,11 @@ class HailoDetector(DetectionApi):
self.inference_engine.run() self.inference_engine.run()
result = self.output_queue.get() result = self.output_queue.get()
if result is None: if result is None:
logging.error("[DETECT_RAW] No inference result received") logger.error("[DETECT_RAW] No inference result received")
return np.zeros((20, 6), dtype=np.float32) return np.zeros((20, 6), dtype=np.float32)
original_input, infer_results = result original_input, infer_results = result
logging.debug("[DETECT_RAW] Inference completed.") logger.debug("[DETECT_RAW] Inference completed.")
# If infer_results is a single-element list, unwrap it. # If infer_results is a single-element list, unwrap it.
if isinstance(infer_results, list) and len(infer_results) == 1: if isinstance(infer_results, list) and len(infer_results) == 1:
@ -363,7 +374,7 @@ class HailoDetector(DetectionApi):
if not isinstance(detection_set, np.ndarray) or detection_set.size == 0: if not isinstance(detection_set, np.ndarray) or detection_set.size == 0:
continue continue
logging.debug(f"[DETECT_RAW] Processing detection set {class_id} with shape {detection_set.shape}") logger.debug(f"[DETECT_RAW] Processing detection set {class_id} with shape {detection_set.shape}")
for det in detection_set: for det in detection_set:
# Expect at least 5 elements: [ymin, xmin, ymax, xmax, confidence] # Expect at least 5 elements: [ymin, xmin, ymax, xmax, confidence]
if det.shape[0] < 5: if det.shape[0] < 5:
@ -372,9 +383,9 @@ class HailoDetector(DetectionApi):
if score < threshold: if score < threshold:
continue continue
if hasattr(self, "labels") and self.labels: if hasattr(self, "labels") and self.labels:
logging.debug(f"[DETECT_RAW] Detected class id: {class_id} -> {self.labels[class_id]}") logger.debug(f"[DETECT_RAW] Detected class id: {class_id} -> {self.labels[class_id]}")
else: else:
logging.debug(f"[DETECT_RAW] Detected class id: {class_id}") logger.debug(f"[DETECT_RAW] Detected class id: {class_id}")
all_detections.append([class_id, score, det[0], det[1], det[2], det[3]]) all_detections.append([class_id, score, det[0], det[1], det[2], det[3]])
@ -392,7 +403,7 @@ class HailoDetector(DetectionApi):
detections_array = np.vstack((detections_array, pad)) detections_array = np.vstack((detections_array, pad))
logging.debug(f"[DETECT_RAW] Processed detections: {detections_array}") logger.debug(f"[DETECT_RAW] Processed detections: {detections_array}")
return detections_array return detections_array
# Preprocess method using inline utility # Preprocess method using inline utility
@ -407,16 +418,16 @@ class HailoDetector(DetectionApi):
# Close the Hailo device # Close the Hailo device
def close(self): def close(self):
logging.debug("[CLOSE] Closing HailoDetector") logger.debug("[CLOSE] Closing HailoDetector")
try: try:
self.inference_engine.hef.close() self.inference_engine.hef.close()
logging.debug("Hailo device closed successfully") logger.debug("Hailo device closed successfully")
except Exception as e: except Exception as e:
logging.error(f"Failed to close Hailo device: {e}") logger.error(f"Failed to close Hailo device: {e}")
raise raise
# ----------------- Configuration Class ----------------- # # ----------------- Configuration Class ----------------- #
class HailoDetectorConfig(BaseDetectorConfig): class HailoDetectorConfig(BaseDetectorConfig):
type: Literal[DETECTOR_KEY] type: Literal[DETECTOR_KEY]
device: str = Field(default="PCIe", title="Device Type") device: str = Field(default="PCIe", title="Device Type")
url: Optional[str] = Field(default=None, title="Custom Model URL") #url: Optional[str] = Field(default=None, title="Custom Model URL")